面向垂直联邦学习中隐私保障的标签撤销学习：无需披露的少样本遗忘
Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure
AI总结：
本文提出一种面向垂直联邦学习中隐私保障的标签撤销学习方法，通过流形混合机制生成合成嵌入，实现高效标签撤销与性能恢复。
摘要：
本文针对垂直联邦学习（VFL）中撤销学习的关键挑战，VFL相较于其水平对应物受到的关注较少。具体而言，我们提出了首个专门针对VFL中标签撤销学习的方法，其中标签在双重角色中作为必要的输入和敏感信息。为此，我们采用表示层面的流形混合机制生成合成嵌入，用于未撤销和保留的样本。这是为了为后续基于梯度的标签遗忘和恢复步骤提供更丰富的信号。这些增强的嵌入随后会经过基于梯度的标签遗忘，从而有效地从模型中移除相关的标签信息。为了在保留的数据上恢复性能，我们引入了一个恢复阶段的优化步骤，以优化剩余的嵌入。这种设计在保持计算效率的同时实现了有效的标签撤销学习。我们通过在多种数据集上的广泛实验验证了我们的方法，包括MNIST、CIFAR-10、CIFAR-100、ModelNet、脑肿瘤MRI、COVID-19放射学和Yahoo Answers，展示了强大的效果和可扩展性。总体而言，这项工作为VFL中的撤销学习开辟了新方向，表明重新想象混合作为高效的机制可以解锁实用且保持效用的撤销学习。代码可在https://github.com/bryanhx/Towards-Privacy-Guaranteed-Label-Unlearning-in-Vertical-Federated-Learning上公开获取。
英文摘要：
This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), a setting that has received far less attention than its horizontal counterpart. Specifically, we propose the first method tailored to \textit{label unlearning} in VFL, where labels play a dual role as both essential inputs and sensitive information. To this end, we employ a representation-level manifold mixup mechanism to generate synthetic embeddings for both unlearned and retained samples. This is to provide richer signals for the subsequent gradient-based label forgetting and recovery steps. These augmented embeddings are then subjected to gradient-based label forgetting, effectively removing the associated label information from the model. To recover performance on the retained data, we introduce a recovery-phase optimization step that refines the remaining embeddings. This design achieves effective label unlearning while maintaining computational efficiency. We validate our method through extensive experiments on diverse datasets, including MNIST, CIFAR-10, CIFAR-100, ModelNet, Brain Tumor MRI, COVID-19 Radiography, and Yahoo Answers demonstrate strong efficacy and scalability. Overall, this work establishes a new direction for unlearning in VFL, showing that re-imagining mixup as an efficient mechanism can unlock practical and utility-preserving unlearning. The code is publicly available at https://github.com/bryanhx/Towards-Privacy-Guaranteed-Label-Unlearning-in-Vertical-Federated-Learning
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
